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            <h1 style="display: none">自然语言处理任务梳理</h1>
            
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                  本文最后更新于：7 个月前
                
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              <h1 id="NLP任务"><a href="#NLP任务" class="headerlink" title="NLP任务"></a>NLP任务</h1><img src="/2020/11/04/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86%E4%BB%BB%E5%8A%A1%E6%A2%B3%E7%90%86/1.png" srcset="/img/loading.gif" class="" title="1.png">
<h2 id="前处理任务"><a href="#前处理任务" class="headerlink" title="前处理任务"></a>前处理任务</h2><p>前处理任务的结果可作为下游任务输入的额外特征。</p>
<h3 id="POSTa-词性标注"><a href="#POSTa-词性标注" class="headerlink" title="POSTa(词性标注)"></a>POSTa(词性标注)</h3><p>往模型中输入句子，对每一个token进行词性的识别。</p>
<p>识别出的词性可以用于下游任务。</p>
<img src="/2020/11/04/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86%E4%BB%BB%E5%8A%A1%E6%A2%B3%E7%90%86/2.png" srcset="/img/loading.gif" class="" title="2.png">
<h3 id="Word-Segmentation-分词"><a href="#Word-Segmentation-分词" class="headerlink" title="Word Segmentation(分词)"></a>Word Segmentation(分词)</h3><p>对于英文，显然句子有天然的分词。所以分词通常是针对中文句子。</p>
<p>分词之后，模型的输入就可以以词汇作单位，而不再以字作单位。</p>
<p>以下面例子做说明：</p>
<p>将一个句子按字输入模型，训练模型来对每个字来进行二分类决定每个字的对应位置输出N或者Y(N/Y是词的边界标识)</p>
<img src="/2020/11/04/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86%E4%BB%BB%E5%8A%A1%E6%A2%B3%E7%90%86/3.png" srcset="/img/loading.gif" class="" title="3.png">
<h3 id="Parsing-语义分析"><a href="#Parsing-语义分析" class="headerlink" title="Parsing(语义分析)"></a>Parsing(语义分析)</h3><p>给定句子产生树状结构——句子的语法结构。</p>
<img src="/2020/11/04/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86%E4%BB%BB%E5%8A%A1%E6%A2%B3%E7%90%86/4.png" srcset="/img/loading.gif" class="" title="4.png">
<h3 id="Coreference-Rosolution-共指消解"><a href="#Coreference-Rosolution-共指消解" class="headerlink" title="Coreference Rosolution(共指消解)"></a>Coreference Rosolution(共指消解)</h3><p>从一段文章或者一段对话中找出指代同一个人或事物的所有词汇。</p>
<img src="/2020/11/04/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86%E4%BB%BB%E5%8A%A1%E6%A2%B3%E7%90%86/5.png" srcset="/img/loading.gif" class="" title="5.png">
<h2 id="具体NLP任务"><a href="#具体NLP任务" class="headerlink" title="具体NLP任务"></a>具体NLP任务</h2><h3 id="Summarization-文本摘要"><a href="#Summarization-文本摘要" class="headerlink" title="Summarization(文本摘要)"></a>Summarization(文本摘要)</h3><p><strong>抽取式摘要：</strong>基于二分类任务，每个句子分开考虑。</p>
<p>衡量文章中句子应不应该放到摘要里面，但是这么做远远不够。</p>
<img src="/2020/11/04/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86%E4%BB%BB%E5%8A%A1%E6%A2%B3%E7%90%86/6.png" srcset="/img/loading.gif" class="" title="6.png">
<p><strong>生成式摘要：</strong>属于seq2seq模型，输入长文本，模型用自己的语言进行短摘要的生成。</p>
<p><strong>模型的copy能力</strong>：输入文本序列和输出摘要很有可能有很多共用词汇，这些共用词汇经过模型的修改整合形成摘要的文本。因此模型需要增加输入copy能力，怎么实现？Pointer network(指针网络)。</p>
<img src="/2020/11/04/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86%E4%BB%BB%E5%8A%A1%E6%A2%B3%E7%90%86/7.png" srcset="/img/loading.gif" class="" title="7.png">
<h3 id="Machine-Translation-机器翻译"><a href="#Machine-Translation-机器翻译" class="headerlink" title="Machine Translation(机器翻译)"></a>Machine Translation(机器翻译)</h3><p><strong>seq2seq</strong></p>
<img src="/2020/11/04/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86%E4%BB%BB%E5%8A%A1%E6%A2%B3%E7%90%86/8.png" srcset="/img/loading.gif" class="" title="8.png">
<p><strong>audio2seq</strong></p>
<img src="/2020/11/04/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86%E4%BB%BB%E5%8A%A1%E6%A2%B3%E7%90%86/9.png" srcset="/img/loading.gif" class="" title="9.png">
<p><strong>audio2audio</strong></p>
<img src="/2020/11/04/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86%E4%BB%BB%E5%8A%A1%E6%A2%B3%E7%90%86/10.png" srcset="/img/loading.gif" class="" title="10.png">
<h3 id="Grammer-Error-Correction-语法纠正"><a href="#Grammer-Error-Correction-语法纠正" class="headerlink" title="Grammer Error Correction(语法纠正)"></a>Grammer Error Correction(语法纠正)</h3><p><strong>seq2seq?</strong> </p>
<img src="/2020/11/04/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86%E4%BB%BB%E5%8A%A1%E6%A2%B3%E7%90%86/11.png" srcset="/img/loading.gif" class="" title="11.png">
<p>不，杀鸡焉用牛刀。</p>
<p><strong>seq2class</strong></p>
<p>输入句子，做分类，输出要对token要做的动作的标识（C/R/A/D)。</p>
<img src="/2020/11/04/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86%E4%BB%BB%E5%8A%A1%E6%A2%B3%E7%90%86/12.png" srcset="/img/loading.gif" class="" title="12.png">
<p>C——复制，保持不变</p>
<p>A——在后面增加词汇</p>
<p>R——置换，把词换个时态或者换成别的词</p>
<p>D——删除</p>
<h3 id="Sentiment-Classfication-情感分类"><a href="#Sentiment-Classfication-情感分类" class="headerlink" title="Sentiment Classfication(情感分类)"></a>Sentiment Classfication(情感分类)</h3><p>输入一段文本或者评论，训练模型，输出文本的情感分类(正面/负面)。</p>
<p><strong>seq2class</strong></p>
<img src="/2020/11/04/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86%E4%BB%BB%E5%8A%A1%E6%A2%B3%E7%90%86/13.png" srcset="/img/loading.gif" class="" title="13.png">
<h3 id="Stance-Detection-立场侦测"><a href="#Stance-Detection-立场侦测" class="headerlink" title="Stance Detection(立场侦测)"></a>Stance Detection(立场侦测)</h3><p><strong>seq2class</strong></p>
<p>通过一则博文或者文章以及其下的评论回复来进行评论者所处立场的判断。</p>
<p>立场通常有四类：SDQC(Support、Denying、Querying and Commenting)。</p>
<p>立场侦测经常被用于事实预测:</p>
<img src="/2020/11/04/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86%E4%BB%BB%E5%8A%A1%E6%A2%B3%E7%90%86/14.png" srcset="/img/loading.gif" class="" title="14.png">
<p><strong>事实预测：</strong></p>
<p><strong>seq2class</strong></p>
<p>根据新闻消息或者博文的评论立场以及外部的知识判断消息或文章内容的真实性。</p>
<h3 id="Natural-Language-Inference-自然语言推理"><a href="#Natural-Language-Inference-自然语言推理" class="headerlink" title="Natural Language Inference(自然语言推理)"></a>Natural Language Inference(自然语言推理)</h3><p><strong>seq2class</strong></p>
<p><strong>推理模型的文本输入</strong>：premise(前提) + hypothesis(假设)</p>
<p><strong>模型输出：</strong>对假设是否成立的判断结果，矛盾/包含(可推得)/中立(contradiction/entailment/neutral)</p>
<img src="/2020/11/04/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86%E4%BB%BB%E5%8A%A1%E6%A2%B3%E7%90%86/15.png" srcset="/img/loading.gif" class="" title="15.png">
<h3 id="Question-Answering-问答"><a href="#Question-Answering-问答" class="headerlink" title="Question Answering(问答)"></a>Question Answering(问答)</h3><h4 id="传统的基于检索的问答系统"><a href="#传统的基于检索的问答系统" class="headerlink" title="传统的基于检索的问答系统"></a>传统的基于检索的问答系统</h4><p><strong>简单的(模组少)：</strong></p>
<img src="/2020/11/04/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86%E4%BB%BB%E5%8A%A1%E6%A2%B3%E7%90%86/16.png" srcset="/img/loading.gif" class="" title="16.png">
<ul>
<li><p>问题处理——对问题进行格式化，检测其答案的类别</p>
</li>
<li><p>检索资料库——进行文档、文章的检索选择</p>
</li>
<li><p>答案的生成和评估——从候选文章中抽取答案，抽取的答案根据第一步检测到的答案类别评估其正确性</p>
</li>
</ul>
<p><strong>复杂的(模组多)：</strong></p>
<img src="/2020/11/04/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86%E4%BB%BB%E5%8A%A1%E6%A2%B3%E7%90%86/17.png" srcset="/img/loading.gif" class="" title="17.png">
<p><strong>和简单架构的区别：</strong></p>
<ul>
<li><p>问题处理——模组更多</p>
</li>
<li><p>候选答案生成——综合检索文章得到的候选答案和从自带的有结构资料库中调取的答案</p>
</li>
<li>答案评分</li>
<li>融合对等答案，返回答案及其可信度</li>
</ul>
<h4 id="基于深度学习的QA"><a href="#基于深度学习的QA" class="headerlink" title="基于深度学习的QA"></a>基于深度学习的QA</h4><p><strong>seq2seq</strong></p>
<p>输入问题文本和外部结构化/无结构化的知识(大多来自搜索引擎)，训练模型得到问题的答案。</p>
<img src="/2020/11/04/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86%E4%BB%BB%E5%8A%A1%E6%A2%B3%E7%90%86/18.png" srcset="/img/loading.gif" class="" title="18.png">
<p>但是要实现直接向模型输入问题和外部知识就生成问题答案还有非常长的一段路要走。目前我们常做的只是从文本中抽取答案。</p>
<p><strong>抽取式QA：</strong></p>
<p><strong>seq2seq</strong></p>
<p>答案就在背景文章里面，向模型输入背景文章和问题，其实就是做通常意义上的阅读理解，模型产生抽取的答案文本在文章中的 start position 和 end position。</p>
<img src="/2020/11/04/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86%E4%BB%BB%E5%8A%A1%E6%A2%B3%E7%90%86/19.png" srcset="/img/loading.gif" class="" title="19.png">
<h3 id="Dialogue-对话"><a href="#Dialogue-对话" class="headerlink" title="Dialogue(对话)"></a>Dialogue(对话)</h3><p>对话涉及到自然语言生成(NLG)和自然语言理解(NLU)</p>
<h4 id="Chatting-闲聊"><a href="#Chatting-闲聊" class="headerlink" title="Chatting(闲聊)"></a>Chatting(闲聊)</h4><p><strong>seq2seq</strong></p>
<p>聊天都是有背景的，所以模型的输入应该是增量式的，模型的输出是根据之前的对话内容产生的。</p>
<p>根据对话的需求可以进行定制：</p>
<img src="/2020/11/04/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86%E4%BB%BB%E5%8A%A1%E6%A2%B3%E7%90%86/20.png" srcset="/img/loading.gif" class="" title="20.png">
<h4 id="Task-oriented-任务导向的对话"><a href="#Task-oriented-任务导向的对话" class="headerlink" title="Task-oriented(任务导向的对话)"></a>Task-oriented(任务导向的对话)</h4><p><strong>seq2seq</strong></p>
<p>需要实现一定的功能，比如提供订票、订餐厅、订酒店等服务</p>
<img src="/2020/11/04/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86%E4%BB%BB%E5%8A%A1%E6%A2%B3%E7%90%86/21.png" srcset="/img/loading.gif" class="" title="21.png">
<p><strong>系统架构</strong></p>
<img src="/2020/11/04/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86%E4%BB%BB%E5%8A%A1%E6%A2%B3%E7%90%86/22.png" srcset="/img/loading.gif" class="" title="22.png">
<p>State Tracker记录当前对话的状态</p>
<h2 id="知识图"><a href="#知识图" class="headerlink" title="知识图"></a>知识图</h2><h3 id="NER-命名实体识别"><a href="#NER-命名实体识别" class="headerlink" title="NER(命名实体识别)"></a>NER(命名实体识别)</h3><p><strong>seq2class</strong></p>
<p>识别出句子中的人名、地名、组织等实体</p>
<img src="/2020/11/04/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86%E4%BB%BB%E5%8A%A1%E6%A2%B3%E7%90%86/23.png" srcset="/img/loading.gif" class="" title="23.png">
<h3 id="RE-关系抽取"><a href="#RE-关系抽取" class="headerlink" title="RE(关系抽取)"></a>RE(关系抽取)</h3><p><strong>seq2class</strong></p>
<p>输入文本和文本中两个实体，训练模型得到两个实体之间的关系</p>
<p>关系的种类基本是固定的，因此关系抽取的模型往往是去做一个复杂的分类任务</p>
<img src="/2020/11/04/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86%E4%BB%BB%E5%8A%A1%E6%A2%B3%E7%90%86/24.png" srcset="/img/loading.gif" class="" title="24.png">
<h2 id="综合任务"><a href="#综合任务" class="headerlink" title="综合任务"></a>综合任务</h2><p>综合任务的意义：看模型是否是真的“理解”了人类语言，能“举一反三”</p>
<h3 id="GLUE"><a href="#GLUE" class="headerlink" title="GLUE"></a>GLUE</h3><p>分为三大类</p>
<ul>
<li>文本分类(语法错误检查、文本情感分析)</li>
<li>文本相似度计算</li>
<li>自然语言推理</li>
</ul>
<img src="/2020/11/04/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86%E4%BB%BB%E5%8A%A1%E6%A2%B3%E7%90%86/25.png" srcset="/img/loading.gif" class="" title="25.png">
<h3 id="Super-GLUE"><a href="#Super-GLUE" class="headerlink" title="Super GLUE"></a>Super GLUE</h3><p>包含8个NLP任务，大多和QA有关</p>
<h3 id="DecaNLP"><a href="#DecaNLP" class="headerlink" title="DecaNLP"></a>DecaNLP</h3><p>同一个模型解决10个NLP任务</p>
<p>怎么实现？往QA的方向改造这些任务</p>
<img src="/2020/11/04/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86%E4%BB%BB%E5%8A%A1%E6%A2%B3%E7%90%86/26.png" srcset="/img/loading.gif" class="" title="26.png">
<h2 id="总结"><a href="#总结" class="headerlink" title="总结"></a>总结</h2><p>根据这些NLP任务的输入输出，把这些任务和任务相关的一些技术手段进行梳理</p>
<img src="/2020/11/04/%E8%87%AA%E7%84%B6%E8%AF%AD%E8%A8%80%E5%A4%84%E7%90%86%E4%BB%BB%E5%8A%A1%E6%A2%B3%E7%90%86/27.png" srcset="/img/loading.gif" class="" title="27.png">
<h3 id="one-on-one"><a href="#one-on-one" class="headerlink" title="one on one"></a>one on one</h3><p><strong>seq2class</strong></p>
<ul>
<li>情感分析</li>
<li>立场检测</li>
<li>文本内容辨真伪</li>
<li>文本意图识别</li>
<li>对话决策</li>
</ul>
<p><strong>seq2tokenclass</strong></p>
<ul>
<li>词性标注</li>
<li>分词</li>
<li>抽取式摘要</li>
<li>命名实体识别</li>
</ul>
<p><strong>seq2seq</strong></p>
<ul>
<li>抽象式摘要</li>
<li>机器翻译</li>
<li>文本语法矫正</li>
<li>自然语言生成</li>
</ul>
<h3 id="n-on-one"><a href="#n-on-one" class="headerlink" title="n on one"></a>n on one</h3><p><strong>seq2class</strong></p>
<ul>
<li>自然语言推理</li>
<li>搜索引擎</li>
<li>关系抽取</li>
</ul>
<p><strong>copy from input</strong></p>
<ul>
<li>抽取式QA</li>
</ul>
<p><strong>seq2seq</strong></p>
<ul>
<li>常规QA</li>
<li>任务导向对话</li>
<li>聊天机器人</li>
<li>State Tracker</li>
</ul>
<h3 id="others"><a href="#others" class="headerlink" title="others"></a>others</h3><ul>
<li>语义分析</li>
<li>共指消解</li>
</ul>

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